{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>标题</th>\n",
       "      <th>浏览量</th>\n",
       "      <th>点赞</th>\n",
       "      <th>城市</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>·【北京】四季十年，千载古都的万种风情</td>\n",
       "      <td>2188</td>\n",
       "      <td>246</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>·600岁紫禁城的画意诗情 | 在故宫三年等一树花开</td>\n",
       "      <td>2515</td>\n",
       "      <td>399</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>·二刷北京环球影城，我和闺蜜实现庆祝生日的约定（攻略吐血整理）</td>\n",
       "      <td>23422</td>\n",
       "      <td>2017</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>荷包牡丹</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>·【饮食男女】功略向：那些乱花渐欲迷人眼的网红之地，值得吗？ 2021北京有趣餐厅小盘点</td>\n",
       "      <td>246</td>\n",
       "      <td>15</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29374</th>\n",
       "      <td>【武汉独行】一座城市的味道。</td>\n",
       "      <td>5990</td>\n",
       "      <td>253</td>\n",
       "      <td>武汉</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29375</th>\n",
       "      <td>2018武汉东湖灯会</td>\n",
       "      <td>1523</td>\n",
       "      <td>378</td>\n",
       "      <td>武汉</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29376</th>\n",
       "      <td>🚣🏽皮划艇采莲蓬</td>\n",
       "      <td>62</td>\n",
       "      <td>7</td>\n",
       "      <td>武汉</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29377</th>\n",
       "      <td>东湖《落雁景区》游记</td>\n",
       "      <td>3419</td>\n",
       "      <td>33</td>\n",
       "      <td>武汉</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29378</th>\n",
       "      <td>嵩阳寺半日游</td>\n",
       "      <td>1349</td>\n",
       "      <td>14</td>\n",
       "      <td>武汉</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29379 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 标题    浏览量    点赞  城市\n",
       "0                               ·【北京】四季十年，千载古都的万种风情   2188   246  北京\n",
       "1                        ·600岁紫禁城的画意诗情 | 在故宫三年等一树花开   2515   399  北京\n",
       "2                   ·二刷北京环球影城，我和闺蜜实现庆祝生日的约定（攻略吐血整理）  23422  2017  北京\n",
       "3                                              荷包牡丹     18     2  北京\n",
       "4      ·【饮食男女】功略向：那些乱花渐欲迷人眼的网红之地，值得吗？ 2021北京有趣餐厅小盘点    246    15  北京\n",
       "...                                             ...    ...   ...  ..\n",
       "29374                                【武汉独行】一座城市的味道。   5990   253  武汉\n",
       "29375                                    2018武汉东湖灯会   1523   378  武汉\n",
       "29376                                      🚣🏽皮划艇采莲蓬     62     7  武汉\n",
       "29377                                    东湖《落雁景区》游记   3419    33  武汉\n",
       "29378                                        嵩阳寺半日游   1349    14  武汉\n",
       "\n",
       "[29379 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_excel('马蜂窝游记.xls')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分词去停用词，并构建词云图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\WX\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.825 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 8400x4200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import jieba\n",
    "import wordcloud\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "#分词以及去停用词\n",
    "stopwords=pd.read_csv('停用词.txt',index_col=False,quoting=3,sep=\"\\t\",names=['stopword'], encoding='utf-8')\n",
    "stopwords=stopwords['stopword'].values\n",
    "def preprocess_text(content_lines, sentences):\n",
    "    #词袋构建函数\n",
    "    for line in content_lines:\n",
    "        try:\n",
    "            segs=jieba.lcut(line)\n",
    "            segs = [v for v in segs if not str(v).isdigit()]#去数字\n",
    "            segs = [v for v in segs if not str(v).encode().isalpha()]#去数字\n",
    "            segs = list(filter(lambda x:x.strip(), segs))   #去左右空格\n",
    "            segs = list(filter(lambda x:x not in stopwords, segs)) #去掉停用词\n",
    "            segs = list(filter(lambda x:len(x)!=1, segs)) #去掉停用词\n",
    "            sentences.append(segs)\n",
    "        except Exception:\n",
    "            continue\n",
    "sentences1 = []\n",
    "preprocess_text(data['标题'].values,sentences1)\n",
    "data['分词后'] = sentences1\n",
    "\n",
    "a = ''\n",
    "for i in sentences1:\n",
    "    a = a + ' '.join(i) +' '\n",
    "\n",
    "mpl.rcParams['font.sans-serif'] = [\"SimHei\"]\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False\n",
    "plt.figure(dpi=600,figsize=(14,7))\n",
    "\n",
    "WC = wordcloud.WordCloud(max_font_size=150,background_color='white',font_path=r'C:\\Windows\\Fonts\\simfang.ttf',collocations=False, width=800,height=400,margin=2)\n",
    "con = WC.generate(a)\n",
    "plt.imshow(con)\n",
    "plt.axis(\"off\")\n",
    "plt.savefig('总词云图.png',dpi=400)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 情感分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from snownlp import SnowNLP\n",
    "sent = []\n",
    "for i in data['标题'].values:\n",
    "    sent.append(SnowNLP(i).sentiments)\n",
    "data['情感得分'] = sent\n",
    "data.to_excel('情感分析结果.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算不同地区的总体平均情感值，判断地区差异"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "tem = data.groupby('城市').mean()['情感得分']\n",
    "#构造数据\n",
    "y = tem.values\n",
    "x = np.arange(len(y))\n",
    "\n",
    "#设置柱状图的宽度\n",
    "width = 0.5\n",
    "\n",
    "#绘图\n",
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "plt.bar(x=x,height=y.astype('float16'),width=width)\n",
    "#添加图标题和图例\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "plt.xticks(x,tem.index)\n",
    "plt.xlabel('地域',size=18)\n",
    "plt.ylabel('情感得分',size=18)\n",
    "plt.legend(fontsize=15)\n",
    "plt.tick_params(labelsize=15)\n",
    "plt.savefig('不同地域的情感得分.png',dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算总体情感分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2103: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os \n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pyecharts.charts import Bar\n",
    "from pyecharts import options as opts\n",
    "#显示中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif字体）\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 步骤二（解决坐标轴负数的负号显示问题）\n",
    "\n",
    "#画图\n",
    "f,ax1 = plt.subplots(1,1,figsize=(18,6))\n",
    "sns.distplot(data['情感得分'].values,ax=ax1, rug=True, bins=20)\n",
    "ax1.tick_params(labelsize=14)\n",
    "ax1.set_xlabel('情感得分', fontsize=16)\n",
    "ax1.set_ylabel('密度', fontsize=16)\n",
    "plt.savefig('分布图.png',dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LDA主题分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\sparse\\sparsetools.py:21: DeprecationWarning: `scipy.sparse.sparsetools` is deprecated!\n",
      "scipy.sparse.sparsetools is a private module for scipy.sparse, and should not be used.\n",
      "  _deprecated()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el1650027814902598886175012727\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el1650027814902598886175012727\", ldavis_el1650027814902598886175012727_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el1650027814902598886175012727\", ldavis_el1650027814902598886175012727_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el1650027814902598886175012727\", ldavis_el1650027814902598886175012727_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "from gensim import corpora\n",
    "from gensim.models import LdaModel\n",
    "\n",
    "texts = sentences1\n",
    "dictionary = corpora.Dictionary(texts)\n",
    "corpus = [dictionary.doc2bow(text) for text in texts]\n",
    "\n",
    "lda = LdaModel(corpus=corpus,num_topics=4)\n",
    "d = pyLDAvis.gensim.prepare(lda, corpus, dictionary)\n",
    "pyLDAvis.save_html(d,'LDA结果.html')\n",
    "pyLDAvis.display(d)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
